Environmental influences on aquatic plants in freshwater ecosystems
Bibliographic record
Abstract
Aquatic plants are important components of many freshwater ecosystems. In this review we examine natural and anthropogenic influences on the distribution and abundance of aquatic plants, and develop a conceptual model of those diverse interactions. Species of aquatic plants vary greatly in their anatomy, physiology, life-history traits, and ability to tolerate inorganic and biological stressors. Key examples of inorganic stressors are extreme regimes of flow velocity, irradiance, salinity, ice cover, temperature, nutrients, and pollutants. Stressors associated with competition, herbivory, and disease may also limit the ability of species to utilize otherwise suitable habitats. Some aquatic plants have a cosmopolitan distribution and display high levels of polymorphism and phenotypic plasticity in response to variations of environmental factors; these qualities allow them to occur over a wide range of conditions. Other species, however, have narrower tolerances and are potentially useful indicators of environmental conditions, in terms of either their presence or relative abundance within communities. In this review, we examine key environmental influences affecting aquatic plants, and their potential use as indicators at local, watershed, and regional scales.Key words: aquatic plants (aquatic macrophytes), environmental factors, environmental indicators, environmental stressors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".